作者机构：
[Hongchen Wang] Research Institute of Scientific and Technological Development, Wuhan University, Wuhan, Hubei, China;[Chang Li; Yanping Zheng] College of Urban and Environmental Science, Central China Normal University, Wuhan, Hubei, China
通讯机构：
[Chang Li] C;College of Urban and Environmental Science, Central China Normal University, Wuhan, Hubei, China
摘要：
On the basis of sufficient understanding about the general situation of the Lu’an planning area, Anhui Province, this paper uses strengths weakness opportunity threats (SWOT) analysis to analyze the internal and external factors of the development of the planning area itself; then it analyzes the industrial cluster situation in the planning area, and uses GIS space analysis to analyze the industry status of the planning area, integrating it with the geographic space expression to provide references for formulating the economic development program of the Lu’an planning area.
作者机构：
[李畅] Key Lab. of Disaster Reduction and Emergency Response Eng. of the Ministry of Civil Affairs, Beijing 100124, China;[孙明伟] School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;[李奇] Center for Earth Observation and Digital Earth Chinese Academy of Sciences, Airborne Remote Sensing Center, Beijing 100101, China;[王欢] Investment Dept. of The Third Construction Eng. Ltd. Liability Co. of China Constr. Third Eng. Bur., Wuhan 430074, China;[王欢; 李畅] College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China
通讯机构：
Key Lab. of Disaster Reduction and Emergency Response Eng. of the Ministry of Civil Affairs, China
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2014年11(8):1394-1398 ISSN：1545-598X
通讯作者：
Li, Chang
作者机构：
[Li, Chang] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.;[Li, Chang] Minist Civil Affairs China, Key Lab Disaster Reduct & Emergency Response Engn, Beijing 100124, Peoples R China.;[Shi, Wenzhong] Hong Kong Polytech Univ, Joint Spatial Informat Res Lab, Hong Kong, Hong Kong, Peoples R China.;[Shi, Wenzhong] Wuhan Univ, Wuhan 430079, Peoples R China.
通讯机构：
[Li, Chang] C;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
Generalized-line-based iterative transformation model (GLBITM);ground control lines (GCLs);imagery registration;iterative method with variable weights;polynomial model;posterior variance estimation
摘要：
Imagery registration and rectification is a process of transforming different sets of data into one coordinate system. A new model, i.e., the generalized-line-based iterative transformation model (GLBITM), is proposed by integrating the line-based transformation model (LBTM) and generalized point photogrammetry (GPP). First, the initial value of an affine transformation is acquired by LBTM. Then, on the basis of ground control lines (GCLs), not ground control points, the linear feature adjustment model with GPP is extended to a quadratic polynomial model and utilized to iteratively solve transformation coefficients. This process eliminates the translation amount and recalculates the scale and rotation coefficients. The authors suggest an iterative method with variable weights that is based on posterior variance estimation to improve quality control. A significant characteristic of the GLBITM is that the two endpoints of the corresponding GCLs are not necessarily conjugate points. The GLBITM integrates the advantages of the LBTM and GPP and avoids their respective shortfalls. Finally, this experiment verifies that the GLBITM gives correct, robust, and effective results that can be applied in high-resolution satellite imagery processing of multiple sensors, angles, and resolutions.
期刊：
IOP Conference Series: Earth and Environmental Science,2014年17(1) ISSN：1755-1307
通讯作者：
Zhou, Wei
作者机构：
[Li, Qi; Zhou, Wei] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China.;[Li, Chang] Huazhong Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Zhou, Wei] C;Chinese Acad Sci, Inst Remote Sensing & Digital Earth, 9 Dengzhuang South Rd, Beijing 100094, Peoples R China.
会议名称：
35th International Symposium on Remote Sensing of Environment (ISRSE35)
会议时间：
APR 22-26, 2013
会议地点：
Inst Remote Sensing & Digital Earth, Beijing, PEOPLES R CHINA
会议主办单位：
Inst Remote Sensing & Digital Earth
会议论文集名称：
IOP Conference Series-Earth and Environmental Science
摘要：
Small-Footprint Airborne LiDAR(light detection and ranging) remote sensing is a breakthrough technology for deriving forest canopy structural characteristics. Because the technique is relatively new as applied to canopy measurement in China, there is a tremendous need for experiments that integrate field work, LiDAR remote sensing and subsequent analyses for retrieving the full complement of structural measures critical for forestry applications. Data storage capacity and high processing speed available today have made it possible to digitally sample and store the entire reflected waveform, instead of only extracting the discrete coordinates which form the so-called point clouds. Return waveforms can give more detailed insights into the vertical structure of surface objects, surface slope, roughness and reflectivity than the conventional echoes. In this paper, an improved Expectation Maximum (EM) algorithm is adopted to decompose raw waveform data. Derived forest biophysical parameters, such as vegetation height, subcanopy topography, crown volume, ground reflectivity, vegetation reflectivity and canopy closure, are able to describe the horizontal and vertical forest canopy structure.
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2014年11(1):210-214 ISSN：1545-598X
通讯作者：
Hao, Ming
作者机构：
[Zhang, Hua; Hao, Ming] China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Peoples R China.;[Shi, Wenzhong] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong, Hong Kong, Peoples R China.;[Li, Chang] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Hao, Ming] C;China Univ Min & Technol, Sch Environm Sci & Spatial Informat, Xuzhou 221116, Peoples R China.
关键词：
Expectation-maximization (EM);level set method;remote sensing;unsupervised change detection
摘要：
The level set method, because of its implicit handling of topological changes and low sensitivity to noise, is one of the most effective unsupervised change detection techniques for remotely sensed images. In this letter, an expectation-maximization-based level set method (EMLS) is proposed to detect changes. First, the distribution of the difference image generated from multitemporal images is supposed to satisfy Gaussian mixture model, and expectation-maximization (EM) is then used to estimate the mean values of changed and unchanged pixels in the difference image. Second, two new energy terms, based on the estimated means, are defined and added into the level set method to detect those changes without initial contours and improve final accuracy. Finally, the improved level set method is implemented to partition pixels into changed and unchanged pixels. Landsat and QuickBird images were tested, and experimental results confirm the EMLS effectiveness when compared to state-of-the-art unsupervised change detection methods.
摘要：
Honghu Lake, one of the seven largest fresh-water lakes in China, is well known for its ecological and economic importance, as well as its rapid changes in recent years. This study investigates the potential of using remote sensing to map and monitor aquatic vegetation changes in Honghu Lake on a large scale. Landsat TM/ETM+ images dated July 27, 2000, July 9, 2002, and July 17, 2008, and CBERS image dated August 12, 2005, are employed to map the aquatic vegetation distribution in the lake. A hybrid classification method, combining the power of the decision tree classifier, naive Bayes classifier, and supporting vector machine classifier is used to distinguish different wetland types. A novel polar coordinate map method is proposed to map the changes of aquatic vegetation on a large scale. The map demonstrates vegetation patch size changes and percentage changes in the whole lake directions during four periods. Validation using in situ surveys and historical ancillary data suggests that this approach could map the distribution and monitor the changes of aquatic vegetation on a large scale efficiently. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI:10.1117/1.JRS.7.073593]
作者机构：
[李畅] College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China;[李芳芳] Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
通讯机构：
[Li, C.] C;College of Urban and Environmental Science, Central China Normal University, China
期刊：
Telkomnika (Telecommunication Computing Electronics and Control),2013年11(12):7462-7469 ISSN：1693-6930
通讯作者：
Li, C.(lichang@mail.ccnu.edu.cn)
作者机构：
[Chang LI] Key Laboratory of Disaster Reduction, Emergency Response Engineering of the Ministry of Civil Affairs, 6 Guangbai East Road, Chaoyang District, Beijing, 100124, China;[Fangfang LI] College of Information Systems and Management, China National University of Defense Technology, Changsha 410073, China;[Wenzhong SHI] Joint Spatial Information Research Laboratory, The Hong Kong Polytechnic University and Wuhan University, Hong Kong and Wuhan, China;[Chang LI] College of Urban and Environmental Science, Central China Normal University, 152 Luoyu Road, Wuhan 430079, China
摘要：
Current methods of remotely sensed image change detection almost assume that the DEM of the surface objects do not change. However, for the geological disasters areas (such as: landslides, mudslides and avalanches, etc.), this assumption does not hold. And the traditional approach is being challenged. Thus, a new theory for change detection needs to be extended from two-dimensional (2D) to three-dimensional (3D) urgently. This paper aims to present an innovative scheme for change detection method, object-oriented simultaneous three-dimensional geometric and physical change detection (OOS3DGPCD) using GIS-guided knowledge. This aim will be reached by realizing the following specific objectives: a) to develop a set of automatic multi-feature matching and registration methods; b) to propose an approach for simultaneous detecting 3D geometric and physical attributes changes based on the object-oriented strategy; c) to develop a quality control method for OOS3DGPCD; d) to implement the newly proposed OOS3DGPCD method by designing algorithms and developing a prototype system. For aerial remotely sensed images of YingXiu, Wenchuan, preliminary experimental results of 3D change detection are shown so as to verify our approach.
期刊：
Journal of Convergence Information Technology,2012年7(19):546-553 ISSN：1975-9320
通讯作者：
Li, F.(lifangfang83@163.com)
作者机构：
[Li, Fangfang] Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China;[Mao, Xingliang] Internet News Management Office of Publicity, Department of Hunan Provincial CCP Committees, Changsha 410011, China;[Xiao, Benlin] Civil Engineering and Architecture School, Hubei University of technology, Wuhan 430068, China;[Li, Chang] College of Urban and Environmental Science, HuaZhong Normal University, Wuhan 430079, China
摘要：
Aquatic vegetation plays an important role in the maintenance of wetland biodiversity and ecological function. As the complex spectral characteristics and growth environment, its spatial distribution is affected by many factors. This study investigated the potential of using remote sensing to map aquatic vegetation distribution on a large scale in Honghu Lake, China. According to aquatic vegetation's ecological characteristics, the study firstly analyzed the selection and extraction of optimal feature images benefiting aquatic vegetation classification. Next, classification knowledge mining based on these feature images was discussed. Finally, a multi-classifier combination method, which combines decision tree classifier, naive bayes classifier and supporting vector machine classifier, was proposed to distinguish different wetland types. Validation using in situ surveys suggested that this approach could get higher accuracy than each single classifier in mapping aquatic vegetation distribution on a large scale.
